mlsanityData to 7 Oct 2026

Knowledge-Aware Meta-learning for Low-Resource Text Classification

arXiv 2109.04707 ↗First posted 10 Sep 2021cs.CL cs.LGcited by 10

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Abstract (arXiv)

Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 29 of its 47 references that are in the map. First posted on arXiv 10 Sep 2021.

Builds on

29 of 47 references.

References · 29

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17 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 1; 2026: 1.

10 papers.

  1. 2022MetaPrompting: Learning to Learn Better Promptscited by 19
  2. 2023Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networkscited by 4
  3. 2024IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classificationcited by 4
  4. 2022Improving Graph-Based Text Representations with Character and Word Level N-gramscited by 2
  5. 2023Few-Shot Table-to-Text Generation with Prompt Planning and Knowledge Memorizationcited by 2
  6. 2023Adapting Knowledge for Few-shot Table-to-Text Generationcited by 2
  7. 2023Retrieval-Augmented Meta Learning for Low-Resource Text Classificationcited by 2
  8. 2023Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledgecited by 1
  9. 2025LLM-based Contrastive Self-Supervised AMR Learning with Masked Graph Autoencoders for Fake News Detectioncited by 0
  10. 2026CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classificationcited by 0